Abstract
Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation.
Highlights
Bayesian phylogeography has emerged as a powerful approach to analyzing virus spread
We highlight how the new method demonstrates significant increases in commonly-reported trends in phylogenies and that the method identifies climate predictors that appear to be consistent with known trends in seasonal trends in influenza
Our work offers valuable insight for researchers wishing to study the evolutionary history of viruses and may prove useful in determining the correct method to choose for a given application of virus phylogeography
Summary
Bayesian phylogeography has emerged as a powerful approach to analyzing virus spread. It utilizes sequence data to perform ancestral reconstruction and estimate the most likely lineages of the viruses in rooted, time-measured phylogenies [1] using nucleotide substitution models, molecular clocks, and coalescent priors under a probabilistic Bayesian framework known as Bayesian stochastic search variable selection (BSSVS) [1,2,3] This framework has improved ancestral state reconstruction and has recently been used to analyze human and animal influenza viruses both globally [4,5] and nationally [6,7]. Studies have followed this approach to uncover the predictors associated with the spread of H5N1 in Egypt [13] and for HIV in Brazil [14] Such studies have demonstrated the utility of combining genetic and geospatial inferences from phylogeography with surveillance data in epidemiological studies like Yang et al [15]. These analyses may enable actionable solutions for public health officials once consistent identification of contributing predictors is achieved
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